Field
Embodiments of the present disclosure generally relate to data management, and more particularly to optimized techniques for managing data in a manufacturing environment using both real-time queries and data modeling.
Description of the Related Art
In the manufacturing industry, the issues of data collection and information quality and thoroughness are increasingly becoming important concerns due, in part, to advances in modern manufacturing systems and the shrinking form factor of the devices. For example, through advances in data collection and data modeling, many manufacturers can accurately predict part failures, system optimizations, and the like. As manufacturing systems become more complex, the quality and completeness of collected data can play a decisive role in determining whether a particular analysis and subsequent action in a process is successful. This concern regarding data and information quality and thoroughness has naturally led to substantial increases in the amount of data that is involved in different manufacturing systems. The substantial rise in the amount of data is partly due to new developments in tool/equipment capabilities, advances in factory automation and drive to increase yields along with shrinking geometries. As such, with the rapid rise in the amount of data, manufacturers face challenges related to the management and use of the large amounts of data.
However, many manufacturing environments also leverage their data collection system for other purposes, e.g., equipment and process monitoring, etc. For example, temperature information collected from various sensors within the manufacturing environment could be queried by real-time monitoring applications for use in monitoring the status of the equipment or the quality of the output from the equipment in the manufacturing environment. As such, many manufacturers require a data storage system that is optimized for both real-time queries and data modeling operations. Thus, there is a need for improved methods for managing data in manufacturing systems that are subject to data that changes constantly and accumulates in large scale.
In manufacturing industries, and in particular, the semi-conductor manufacturing industry, data requirements on data volumes, rates, quality, merging and analytics continue to increase, causing the rapid explosion in the amount of data that is used within these industries. Some of these requirements are due, in part, to advances in factory automation, improved tool capabilities, and the drive to improve yields. For example, advances in factory automation and improved tool capabilities, in general, have enabled the manufacture of semi-conductor chips and other various electronic devices that continue to shrink in size. However, this level of manufacturing typically requires high levels of precision of control which increases the amount of data that has to be monitored and analyzed within the manufacturing system. Additionally, the drive to improve yields has, in general, led to the incorporation of multiple systems within manufacturing environments that have allowed for more accurate models to be achieved. However, this also has tended to increase the amount of data as there are more co-existing systems in which data has to be monitored in order to produce accurate models. As such, manufacturers increasingly face challenges related to managing the large amounts of data within these systems.
Traditional techniques typically attempt to address these challenges with existing systems, such as relational database systems. However, in many cases, traditional relational technologies are simply unable to handle the large amounts of data involved in these advanced manufacturing systems. Further, even in cases where relational databases could handle large data sets, the use of relational databases would be cost-prohibitive due the large amounts of time that would be involved in the process of storing large sets of data and processing queries for the data from the relational database. Additionally, storage systems that are designed for processing large data volumes can be used (e.g., distributed filing systems). However, while these systems can be well-suited for extensive data mining, these systems are not capable of providing adequate real-time monitoring and analysis of advanced manufacturing systems.